project_id

Yes

String

Project ID. For details on how to obtain the project ID, see Obtaining a Project ID.

resource_id

Yes

String

Resource ID (data source ID or scenario ID)

workspace_id

Yes

String

Workspace ID

category

Yes

String

Category. The options are:

• RECALL indicates the retrieval job.

• DATASOURCE indicates the data source job.

• FILTER indicates the filter job.

• SORTING indicates the ranking job.

• EVALUATE indicates the effect evaluation job.

Content-Type

No

String

Content type. The value is application/json.

X-Auth-Token

Yes

String

User token. For details on how to obtain the user token, see Obtaining a User Token Through Password Authentication.

is_success

Boolean

Whether the request is successful

jobs

Array of jobs objects

Job details

message

String

Response message (This field is not returned when the request is successful.)

error_code

String

Error code (This parameter is not returned when the request is successful.)

category

String

Category

config_info

String

Configuration information

description

String

Description

job_id

String

Job ID

job_name

String

Job name

job_type

String

Job type

next_schedule_time

Integer

Next scheduling time

platform

String

Platform

resource_id

String

Resource ID

schedule

String

Scheduling parameter

status

String

Status

workspace_id

String

Workspace ID

job_config

jobConfig object

Job settings

nearline_recall_param

NearLineRecallParam object

Parameters of a nearline retrieval job (required for nearline retrieval jobs)

max_recommended_num

Integer

Max. number of candidate sets (required for retrieval jobs)

Minimum: 1

Maximum: 200

match_feature_pairs

Array of MatchFeaturePair objects

Matched feature pair (required for feature matching-based retrieval jobs)

striping

Striping object

Row-based strategy (required for feature matching-based retrieval job, itemCF retrieval job, and userCF retrieval job)

match_type

String

Matching type (required for feature matching-based retrieval jobs). The options are:

• UI: recommending items based on users

• UU: recommending users based on users

• II: recommending items based on items

• IU: recommending users based on items

matrix_factorization

MatrixFactorization object

Matrix decomposition parameter settings (required for the ALS-based MF jobs)

behavior_frequencys

Array of BehaviorFrequency objects

Behavior frequency information (required for historical behavior-based candidate set generation jobs)

file_path

String

File path (required for business rule - manual import retrieval jobs)

ucb_param

UcbParam object

UCB job parameter (required for UCB-based retrieval jobs)

behavior_gravity

BehaviorGravity object

Gravity decay factor (required for the comprehensive behavior popularity-based retrieval jobs)

category

Category object

Type (required for the comprehensive behavior popularity-based retrieval jobs)

behavior_logic

String

Behavior filter logic (required for historical behavior filter jobs). The options are:

• AND: If both conditions are met, the data is filtered.

• OR: If one condition is met, the data is filtered.

features_engineering

EtlBasicParameter object

Feature parameter (required for offline feature engineering jobs)

sample_param

SampleParam object

Sample parameter (required for offline feature engineering jobs)

deep_learning_parameters

DeepLearingParam object

General parameters of a ranking job (required by LR, DeepFM, and AutoGroup)

algorithm_specify_parameters

AlgorithmSpecifyParameters object

Specific parameter of a ranking algorithm (required for LR, DeepFM, and AutoGroup)

load_widetable

Boolean

Importing a wide table (required for offline data import jobs)

load_profile

Boolean

Importing a profile (required for offline data import jobs)

save_mode

String

Retaining a wide table (required for offline data import jobs). The options are:

• append: Yes

• new: No -Overwrite: Overwrite

indicators

Array of Indicator objects

Statistical indicator (required for effect evaluation jobs)

offline_rank_job_name

String

Name of an offline ranking job (required for online training jobs)

update_interval

Integer

Update interval (required for online training jobs)

optimizer

Optimizer object

Optimizer (required for online training jobs)

flows

Flow object

Online process flow (required for online training jobs)

time_limit

Boolean

Time filter

timeFeature

String

Time feature

retainDays

Integer

Retention period (days)

recall_fileds

Array of RecallFiled objects

Retrieved field

itemCF_job_name

String

Name of an itemCF job

name

String

Field name

value

Integer

Number of used field values

Minimum: 1

Maximum: 10

Default: 1

user_feature_name

String

User feature

item_feature_name

String

Item feature

weight

Double

Weight

match_count

Boolean

Measurement of the number of matched tags

nearest_neighborhood

Integer

Nearest neighbors

band

Integer

Similarity degree

Minimum: 1

Maximum: 20

row

Integer

Similarity distance

Minimum: 1

Maximum: 10

implicit_vector_rank

Integer

Embedding size

Minimum: 1

Maximum: 100

regular_param

Double

Optimization lambda

Minimum: 1.0E-8

Maximum: 1

max_iterator_num

Integer

Number of iterations

Minimum: 1

Maximum: 50

behavior_type

String

Behavior type. The options are:

• view indicates that you browsed an item.

• click indicates that you clicked an item.

• collect indicates that you added an item to favorites.

• uncollect indicates that you removed an item from favorites.

• search_click indicates that you searched and clicked an item.

• comment indicates that you made comments on an item.

• share indicates that you shared an item with others.

• like indicates that you gave an item a thumb-up.

• dislike indicates that you gave an item a thumb-down.

• grade indicates that you rated an item.

• consume indicates that you bought an item (primarily refers to goods).

• use indicates that you watched videos/listened to music/read books.

• download indicates that you downloaded something. -tip indicates that you gave somebody or something a reward.

• subscribe indicates that you followed somebody or something.

lower_limit

Integer

Min. times

Minimum: 1

upper_limit

Integer

Max. times

Minimum: 1

time_interval

Integer

Time range

Minimum: 1

alpha

Double

Tradeoff parameter

Minimum: 0

Maximum: 1

min_used_num

Integer

Min. number of behaviors

Minimum: 30

Maximum: 1000

weaken_factor

Double

Decay factor

Minimum: 0.1

Maximum: 5

view_type

String

Behavior quantity counting mode. The options are:

• pv: page views or clicks, measuring the number of web pages visited by website users.

• uv: unique visitors, counting the number of individual users accessing a site within the reporting period.

algo_type

String

Algorithm type. The options are:

• normal

• time

user_meta_list

Array of strings

User feature

item_meta_list

Array of strings

Item feature

user_features

Array of FeatureTransformation objects

User feature

item_features

Array of FeatureTransformation objects

Item feature

rank_etl_filter

RankETLFilter object

Filter parameter

attr

Attribute object

Feature

discrete_method

String

Discrete method. The options are:

• equal_distance_discrete indicates equal-distance discretization.

• user_define_discrete indicates custom discrete distance. -normalize indicates normalization.

• null indicates non-discretization.

params

Object

Specific processing parameter

name

String

Name

data_type

String

Data type

other_uses

Array of strings

Other usage

filter_type

String

Behavior deduplication mode. The options are:

• abs_weight indicates the absolute weight value.

• date indicates the date.

time_type

String

Time type. The options are: Day Week Month

is_monday_first

Boolean

Whether Monday is the first day

divide_type

String

Division mode of training and test sets. The options are:

• TIME indicates the time ratio.

• RAMDOM indicates the number ratio.

train_rate

Double

Training data ratio

Minimum: 0.01

Maximum: 1

test_rate

Double

Test data ratio

Minimum: 0.01

Maximum: 1

initial_parameters

Initial object

Initialization parameter

optimize_parameters

Optimizer object

Optimization parameter

regular_parameters

Regular object

Lambda parameter

max_iterations

Integer

Max. iterations

Minimum: 1

Maximum: 1000

early_stop_iterations

Integer

Iterations at early stopping

Minimum: 1

Maximum: 1000

batch_size

Integer

Batch size

Minimum: 1

dataset_split_parts

Integer

Number of training datasets to be split

Minimum: 1

Maximum: 10

restart_train

Boolean

Retraining

initial_method

String

Initialization method

Enumeration values:

• normal

• uniform

• xavier

mean_value

Double

Mean

Minimum: -1

Maximum: 1

standard_deviation

Double

Standard deviation

Minimum: 0

Maximum: 1

min_value

Double

Min. value

Minimum: -1

Maximum: 0

max_value

Double

Max. value

Minimum: 0

Maximum: 1

l2_regularization

Double

Lambda 2

Minimum: 0

Maximum: 1

regular_loss_compute_mode

String

Regular loss calculation mode

Enumeration values:

• full

• batch

embed_l2_regularization

Double

Lambda 2 of embedding size

Minimum: 0

Maximum: 1

wide_l2_regularization

Double

Lambda 2 of the wide part

Minimum: 0

Maximum: 1

structure_l2_regularization

Double

Lambda 2 of the structured part

Minimum: 0

Maximum: 1

latent_vector_length

Integer

Embedding size (required for DeepFM)

Minimum: 1

Maximum: 100

architecture

Array of integers

Neural network structure (required for DeepFM)

active_function

String

Activation function (required for DeepFM and AutoGroup)

Enumeration values:

• relu

• sigmoid

• tanh

value_keep_probability

Double

Neuron retention probability (required for DeepFM and AutoGroup)

Minimum: 0

Maximum: 1

embed_size

Array of integers

Embedding size of each degree (required for AutoGroup)

mlp_architecture

Array of integers

Neural network structure (required for AutoGroup)

max_order

Integer

Max. interactions (required for AutoGroup)

hash_sizes

Array of integers

Hash length (required for AutoGroup)

hash_compensation

Array of numbers

Feature interaction penalty coefficient (required for AutoGroup)

use_wide_part

Boolean

Wide part required (required for AutoGroup)

structure_optimizer

Optimizer object

Optimizer parameter (required for AutoGroup)

merge_multi_hot

Boolean

Merge multi-value feature (required for AutoGroup)

fix_structure

Boolean

Fix hash structure (required for AutoGroup)

indicator_name

String

Indicator. The options are: PV UV Custom

indicator_params

IndicatorParam object

Indicator parameter (required for custom metric)

customize_parameter

CustomizeParameter object

Custom parameter

customize_formula

CustomizeFormula object

Custom formula

alias

String

Alias

behavior_type

String

Behavior type

threshold

Double

Threshold

Minimum: 0

Maximum: 1

deduplication

String

Deduplication

alias

String

Alias

formula

String

Formula

type

String

Optimizer type

Enumeration values:

• adam

• adagrad

• ftrl

learning_rate

Integer

Learning rate

Minimum: 0

Maximum: 1

initial_accumulator_value

Double

Initial gradient sum

Minimum: 0

Maximum: 1

lambda1

Double

Lambda 1

Minimum: 0

Maximum: 1

lambda2

Double

Lambda 2

Minimum: 0

Maximum: 1

epsilon

Double

Epsilon

Minimum: 0

Maximum: 1

decay_rate

Double

Decay factor

Minimum: 0

Maximum: 1

decay_steps

Double

Decay step

Minimum: 1

flow_id

String

Process flow ID

attr_pair_rules_filter

Array of AttrPairRules objects

Feature pair filter

attr_pair_rules_reserve

Array of AttrPairRules objects

Feature pair to be reserved

deduplication_list

Array of Deduplication objects

Feature deduplication

attribute_info

AttributeInfo object

Comprehensive ranking information

bloom_filter_conf

BloomFilterConf object

Bloom filter settings

group_attr

String

The scatter attribute for grouping

pre_deal

Boolean

Deduplication before ranking

rank_setting

String

Ranking configuration information

rules

Rule object

Candidate set merging

filter_sets

Array of strings

Filter configuration information

attr_value_rules_filter

Array of AttrValueRules objects

Feature filter

attr_value_rules_reserve

Array of AttrValueRules objects

Feature to be reserved

ctr_job

String

Ranking job (required when click-through rate is used)

ratio

Integer

Traffic proportion

Minimum: 1

Maximum: 100

toppings

Array of strings

List of candidate sets to be pinned on top

attr_pairs

Array of AttrPair objects

Feature pair

party_a

String

Feature name of the recommended item

party_b

String

Feature name of the recommended item

attributes

Array of strings

Feature

rank_feature_pairs

Array of RankFeaturePair objects

Matched feature pair

numerical_attrs

Array of NumericalAttr objects

Feature weight

num_statistics_type

String

Statistics mode. The options are:

• ORDER indicates that weights are accumulated based on the feature value sequence.

• ABS indicates that weights are accumulated based on the absolute value.

Enumeration values:

• ORDER

• ABS

feature_name_a

String

Feature of the item to be recommended

feature_name_b

String

Feature of the recommended item

weight

Float

Weight

Minimum: 0.01

Maximum: 1

name

String

Feature name

weight

Float

Weight

Minimum: 0.001

Maximum: 1

behaviors

Array of strings

Type of the behavior to be filtered

interval

Integer

Filter time

Minimum: 1

Maximum: 7

table_name

String

Table name of a candidate set

rule_ratio

Integer

Rule ratio

Minimum: 1

Maximum: 100

priority

Integer

Priority

Minimum: 1

Maximum: 10

attr_values_a

Array of AttrValue objects

Feature value settings of the recommended item

attr_values_b

Array of AttrValue objects

Feature value settings of the item to be recommended

name

String

Feature name

value

String

Feature value

200

OK